U.S. patent number 11,100,111 [Application Number 17/226,423] was granted by the patent office on 2021-08-24 for real-time streaming data ingestion into database tables.
This patent grant is currently assigned to Snowflake Inc.. The grantee listed for this patent is Snowflake Inc.. Invention is credited to Tyler Arthur Akidau, Istvan Cseri, Tyler Jones, Daniel E. Sotolongo, Zhuo Zhang.
United States Patent |
11,100,111 |
Akidau , et al. |
August 24, 2021 |
Real-time streaming data ingestion into database tables
Abstract
A streaming ingest platform can improve latency and expense
issues related to uploading data into a cloud data system. The
streaming ingest platform can organize the data to be ingested into
per-table chunks and per-account blobs. This data may be committed
and may be made available for query processing before it is
ingested into the target source tables. This significantly improves
latency issues. The streaming ingest platform can also accommodate
uploading data from various sources with different processing and
communication capabilities, such as Internet of Things (IOT)
devices.
Inventors: |
Akidau; Tyler Arthur (Seattle,
WA), Cseri; Istvan (Seattle, WA), Jones; Tyler
(Redwood City, CA), Sotolongo; Daniel E. (Seattle, WA),
Zhang; Zhuo (Kirkland, WA) |
Applicant: |
Name |
City |
State |
Country |
Type |
Snowflake Inc. |
Bozeman |
MT |
US |
|
|
Assignee: |
Snowflake Inc. (Bozeman,
MT)
|
Family
ID: |
77389847 |
Appl.
No.: |
17/226,423 |
Filed: |
April 9, 2021 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F
16/24544 (20190101); G06F 16/24568 (20190101); G06F
16/2456 (20190101); G06F 16/2219 (20190101); G06F
16/258 (20190101) |
Current International
Class: |
G06F
16/245 (20190101); G06F 16/2455 (20190101); G06F
16/2453 (20190101) |
Field of
Search: |
;707/714 |
References Cited
[Referenced By]
U.S. Patent Documents
Primary Examiner: Jami; Hares
Attorney, Agent or Firm: Schwegman Lundberg & Woessner,
P.A.
Claims
What is claimed is:
1. A method comprising: receiving data from a client via one or
more channels for ingestion into one or more source tables in a
data system; storing the received data in a storage in a first
format; based on a registration request for the received data,
committing, by a processor, the received data stored in the storage
and making the received data in the first format accessible for
query processing before the received data is ingested into the one
or more source tables; ingesting the received data into the one or
more source tables in a second format; writing the received data to
a metadata store; generating a hybrid table for query processing,
the hybrid table including the committed data in the first format
and data from the one or more source tables in the second format;
and for query processing: converting the committed data from the
first format into a common format; converting the data from the one
or more source tables into the common format; joining the committed
data in the common format and the data from the one or more source
tables in the common format to generate joined data; and executing
a query based on the joined data.
2. The method of claim 1, wherein the received data is organized
into per-table sets, data in each set belonging to a single source
table.
3. The method of claim 2, wherein per-table sets are organized into
per-account groups, data in each group belonging to a single
account.
4. The method of claim 1, further comprising: retrieving expression
properties of the received data; and pruning the received data
based on the expression properties and the query.
5. The method of claim 1, wherein the received data includes
sequencing information.
6. The method of claim 5, wherein ordering of the received data is
maintained based on the sequencing information.
7. A machine-storage medium embodying instructions that, when
executed by a machine, cause the machine to perform operations
comprising: receiving data from a client via one or more channels
for ingestion into one or more source tables in a data system;
storing the received data in a storage in a first format; based on
a registration request for the received data, committing, by a
processor, the received data stored in the storage and making the
received data in the first format accessible for query processing
before the received data is ingested into the one or more source
tables; ingesting the received data into the one or more source
tables in a second format; writing the received data to a metadata
store; generating a hybrid table for query processing, the hybrid
table including the committed data in the first format and data
from the one or more source tables in the second format; and for
query processing: converting the committed data from the first
format into a common format; converting the data from the one or
more source tables into the common format; joining the committed
data in the common format and the data from the one or more source
tables in the common format to generate joined data; and executing
a query based on the joined data.
8. The machine-storage medium of claim 7, wherein the received data
is organized into per-table sets, data in each set belonging to a
single source table.
9. The machine-storage medium of claim 8, wherein per-table sets
are organized into per-account groups, data in each group belonging
to a single account.
10. The machine-storage medium of claim 7, further comprising:
retrieving expression properties of the received data; and pruning
the received data based on the expression properties and the
query.
11. The machine-storage medium of claim 7, wherein the received
data includes sequencing information.
12. The machine-storage medium of claim 11, wherein ordering of the
received data is maintained based on the sequencing
information.
13. A system comprising: at least one hardware processor; and at
least one memory storing instructions that, when executed by the at
least one hardware processor, cause the at least one hardware
processor to perform operations comprising: receiving data from a
client via one or more channels for ingestion into one or more
source tables in a data system; storing the received data in a
storage in a first format; based on a registration request for the
received data, committing, by a processor, the received data stored
in the storage and making the received data in the first format
accessible for query processing before the received data is
ingested into the one or more source tables; ingesting the received
data into the one or more source tables in a second format; writing
the received data to a metadata store; generating a hybrid table
for query processing, the hybrid table including the committed data
in the first format and data from the one or more source tables in
the second format; and for query processing: converting the
committed data from the first format into a common format;
converting the data from the one or more source tables into the
common format; joining the committed data in the common format and
the data from the one or more source tables in the common format to
generate joined data; and executing a query based on the joined
data.
14. The system of claim 13, wherein the received data is organized
into per-table sets, data in each set belonging to a single source
table.
15. The system of claim 14, wherein per-table sets are organized
into per-account groups, data in each group belonging to a single
account.
16. The system of claim 13, the operations further comprising:
retrieving expression properties of the received data; and pruning
the received data based on the expression properties and the
query.
17. The system of claim 13, wherein the received data includes
sequencing information.
18. The system of claim 17, wherein ordering of the received data
is maintained based on the sequencing information.
Description
TECHNICAL FIELD
The present disclosure generally relates to a data systems, such as
databases, and, more specifically. to real-time streaming data
ingestion into a data system.
BACKGROUND
Data systems, such as database systems, may be provided through a
cloud platform, which allows organizations and users to store,
manage, and retrieve data from the cloud. A variety of techniques
can be employed for uploading and storing data in a database or
table in a cloud platform.
To upload data into a data system, conventional systems typically
use an "insert" or "copy" command. For example, a user can copy new
data using a "copy" command, which also necessitates the use of a
running warehouse for transferring the data to the target table.
This conventional approach suffers from significant drawbacks.
These commands must be manually initiated by a user. This manual
initiation can cause latency issues with respect to how fresh the
data is in the target table, depending on how often the commands
are initiated. This manual initiation can also cause some or all
the data to be lost if the task fails. Also, operating a running
warehouse to perform these commands typically incurs large
expenses. Some automated techniques suffer from similar drawbacks
of low latency and high expenses.
BRIEF DESCRIPTION OF THE DRAWINGS
Various ones of the appended drawings merely illustrate example
embodiments of the present disclosure and should not be considered
as limiting its scope.
FIG. 1 illustrates an example computing environment in which a
network-based data warehouse system can implement streams on shared
database objects, according to some example embodiments.
FIG. 2 is a block diagram illustrating components of a compute
service manager, according to some example embodiments.
FIG. 3 is a block diagram illustrating components of an execution
platform, according to some example embodiments.
FIG. 4 shows a computing environment, according to some example
embodiments.
FIG. 5 shows a flow diagram of a method for generating a blob,
according to some example embodiments.
FIG. 6A shows a blob configuration, according to some example
embodiments.
FIG. 6B shows a blob configuration, according to some example
embodiments.
FIG. 7 shows a flow diagram of a method for committing a blob,
according to some example embodiments.
FIG. 8 shows a computing environment, according to some example
embodiments.
FIG. 9 shows a flow diagram of a method for generating and
committing a blob, according to some example embodiments.
FIG. 10 shows a flow diagram of a method for processing a query,
according to some example embodiments
FIG. 11 illustrates a diagrammatic representation of a machine in
the form of a computer system within which a set of instructions
may be executed for causing the machine to perform any one or more
of the methodologies discussed herein, in accordance with some
embodiments of the present disclosure.
DETAILED DESCRIPTION
The description that follows includes systems, methods, techniques,
instruction sequences, and computing machine program products that
embody illustrative embodiments of the disclosure. In the following
description, for the purposes of explanation, numerous specific
details are set forth in order to provide an understanding of
various embodiments of the inventive subject matter. It will be
evident, however, to those skilled in the art, that embodiments of
the inventive subject matter may be practiced without these
specific details. In general, well-known instruction instances,
protocols, structures, and techniques are not necessarily shown in
detail.
A streaming ingest platform, as described herein, can improve
latency and expense issues related to uploading data into a cloud
data system. The streaming ingest platform can organize the data to
be ingested into per-table chunks in a per-account blob. This data
may be committed and then made available for query processing
before it is ingested into the target source tables (e.g.,
converted to target source table format). This significantly
improves latency issues. The streaming ingest platform can also
accommodate uploading data from various sources with different
processing and communication capabilities, such as Internet of
Things (IOT) devices. Moreover, the streaming ingest platform may
upload the data asynchronously to provide more flexible
control.
FIG. 1 illustrates an example shared data processing platform 100
implementing secure messaging between deployments, in accordance
with some embodiments of the present disclosure. To avoid obscuring
the inventive subject matter with unnecessary detail, various
functional components that are not germane to conveying an
understanding of the inventive subject matter have been omitted
from the figures. However, a skilled artisan will readily recognize
that various additional functional components may be included as
part of the shared data processing platform 100 to facilitate
additional functionality that is not specifically described
herein.
As shown, the shared data processing platform 100 comprises the
network-based data warehouse system 102, a cloud computing storage
platform 104 (e.g., a storage platform, an AWS.RTM. service,
Microsoft Azure.RTM., or Google Cloud Services.RTM.), and a remote
computing device 106. The network-based data warehouse system 102
is a network-based system used for storing and accessing data
(e.g., internally storing data, accessing external remotely located
data) in an integrated manner, and reporting and analysis of the
integrated data from the one or more disparate sources (e.g., the
cloud computing storage platform 104). The cloud computing storage
platform 104 comprises a plurality of computing machines and
provides on-demand computer system resources such as data storage
and computing power to the network-based data warehouse system 102.
While in the embodiment illustrated in FIG. 1, a data warehouse is
depicted, other embodiments may include other types of databases or
other data processing systems.
The remote computing device 106 (e.g., a user device such as a
laptop computer) comprises one or more computing machines (e.g., a
user device such as a laptop computer) that execute a remote
software component 108 (e.g., browser accessed cloud service) to
provide additional functionality to users of the network-based data
warehouse system 102. The remote software component 108 comprises a
set of machine-readable instructions (e.g., code) that, when
executed by the remote computing device 106, cause the remote
computing device 106 to provide certain functionality. The remote
software component 108 may operate on input data and generates
result data based on processing, analyzing, or otherwise
transforming the input data. As an example, the remote software
component 108 can be a data provider or data consumer that enables
database tracking procedures, such as streams on shared tables and
views, as discussed in further detail below.
The network-based data warehouse system 102 comprises an access
management system 110, a compute service manager 112, an execution
platform 114, and a database 116. The access management system 110
enables administrative users to manage access to resources and
services provided by the network-based data warehouse system 102.
Administrative users can create and manage users, roles, and
groups, and use permissions to allow or deny access to resources
and services. The access management system 110 can store shared
data that securely manages shared access to the storage resources
of the cloud computing storage platform 104 amongst different users
of the network-based data warehouse system 102, as discussed in
further detail below.
The compute service manager 112 coordinates and manages operations
of the network-based data warehouse system 102. The compute service
manager 112 also performs query optimization and compilation as
well as managing clusters of computing services that provide
compute resources (e.g., virtual warehouses, virtual machines, EC2
clusters). The compute service manager 112 can support any number
of client accounts such as end users providing data storage and
retrieval requests, system administrators managing the systems and
methods described herein, and other components/devices that
interact with compute service manager 112.
The compute service manager 112 is also coupled to database 116,
which is associated with the entirety of data stored on the shared
data processing platform 100. The database 116 stores data
pertaining to various functions and aspects associated with the
network-based data warehouse system 102 and its users.
In some embodiments, database 116 includes a summary of data stored
in remote data storage systems as well as data available from one
or more local caches. Additionally, database 116 may include
information regarding how data is organized in the remote data
storage systems and the local caches. Database 116 allows systems
and services to determine whether a piece of data needs to be
accessed without loading or accessing the actual data from a
storage device. The compute service manager 112 is further coupled
to an execution platform 114, which provides multiple computing
resources (e.g., virtual warehouses) that execute various data
storage and data retrieval tasks, as discussed in greater detail
below.
Execution platform 114 is coupled to multiple data storage devices
124-1 to 124-N that are part of a cloud computing storage platform
104. In some embodiments, data storage devices 124-1 to 124-N are
cloud-based storage devices located in one or more geographic
locations. For example, data storage devices 124-1 to 124-N may be
part of a public cloud infrastructure or a private cloud
infrastructure. Data storage devices 124-1 to 124-N may be hard
disk drives (HDDs), solid state drives (SSDs), storage clusters,
Amazon S3 storage systems or any other data storage technology.
Additionally, cloud computing storage platform 104 may include
distributed file systems (such as Hadoop Distributed File Systems
(HDFS)), object storage systems, and the like.
The execution platform 114 comprises a plurality of compute nodes
(e.g., virtual warehouses). A set of processes on a compute node
executes a query plan compiled by the compute service manager 112.
The set of processes can include: a first process to execute the
query plan; a second process to monitor and delete micro-partition
files using a least recently used (LRU) policy, and implement an
out of memory (OOM) error mitigation process; a third process that
extracts health information from process logs and status
information to send back to the compute service manager 112; a
fourth process to establish communication with the compute service
manager 112 after a system boot; and a fifth process to handle all
communication with a compute cluster for a given job provided by
the compute service manager 112 and to communicate information back
to the compute service manager 112 and other compute nodes of the
execution platform 114.
The cloud computing storage platform 104 also comprises an access
management system 118 and a web proxy 120. As with the access
management system 110, the access management system 118 allows
users to create and manage users, roles, and groups, and use
permissions to allow or deny access to cloud services and
resources. The access management system 110 of the network-based
data warehouse system 102 and the access management system 118 of
the cloud computing storage platform 104 can communicate and share
information so as to enable access and management of resources and
services shared by users of both the network-based data warehouse
system 102 and the cloud computing storage platform 104. The web
proxy 120 handles tasks involved in accepting and processing
concurrent API calls, including traffic management, authorization
and access control, monitoring, and API version management. The web
proxy 120 provides HTTP proxy service for creating, publishing,
maintaining, securing, and monitoring APIs (e.g., REST APIs).
In some embodiments, communication links between elements of the
shared data processing platform 100 are implemented via one or more
data communication networks. These data communication networks may
utilize any communication protocol and any type of communication
medium. In some embodiments, the data communication networks are a
combination of two or more data communication networks (or
sub-Networks) coupled to one another. In alternative embodiments,
these communication links are implemented using any type of
communication medium and any communication protocol.
As shown in FIG. 1, data storage devices 124-1 to 124-N are
decoupled from the computing resources associated with the
execution platform 114. That is, new virtual warehouses can be
created and terminated in the execution platform 114 and additional
data storage devices can be created and terminated on the cloud
computing storage platform 104 in an independent manner. This
architecture supports dynamic changes to the network-based data
warehouse system 102 based on the changing data storage/retrieval
needs as well as the changing needs of the users and systems
accessing the shared data processing platform 100. The support of
dynamic changes allows network-based data warehouse system 102 to
scale quickly in response to changing demands on the systems and
components within network-based data warehouse system 102. The
decoupling of the computing resources from the data storage devices
124-1 to 124-N supports the storage of large amounts of data
without requiring a corresponding large amount of computing
resources. Similarly, this decoupling of resources supports a
significant increase in the computing resources utilized at a
particular time without requiring a corresponding increase in the
available data storage resources. Additionally, the decoupling of
resources enables different accounts to handle creating additional
compute resources to process data shared by other users without
affecting the other users' systems. For instance, a data provider
may have three compute resources and share data with a data
consumer, and the data consumer may generate new compute resources
to execute queries against the shared data, where the new compute
resources are managed by the data consumer and do not affect or
interact with the compute resources of the data provider.
Compute service manager 112, database 116, execution platform 114,
cloud computing storage platform 104, and remote computing device
106 are shown in FIG. 1 as individual components. However, each of
compute service manager 112, database 116, execution platform 114,
cloud computing storage platform 104, and remote computing
environment may be implemented as a distributed system (e.g.,
distributed across multiple systems/platforms at multiple
geographic locations) connected by APIs and access information
(e.g., tokens, login data). Additionally, each of compute service
manager 112, database 116, execution platform 114, and cloud
computing storage platform 104 can be scaled up or down
(independently of one another) depending on changes to the requests
received and the changing needs of shared data processing platform
100. Thus, in the described embodiments, the network-based data
warehouse system 102 is dynamic and supports regular changes to
meet the current data processing needs.
During typical operation, the network-based data warehouse system
102 processes multiple jobs (e.g., queries) determined by the
compute service manager 112. These jobs are scheduled and managed
by the compute service manager 112 to determine when and how to
execute the job. For example, the compute service manager 112 may
divide the job into multiple discrete tasks and may determine what
data is needed to execute each of the multiple discrete tasks. The
compute service manager 112 may assign each of the multiple
discrete tasks to one or more nodes of the execution platform 114
to process the task. The compute service manager 112 may determine
what data is needed to process a task and further determine which
nodes within the execution platform 114 are best suited to process
the task. Some nodes may have already cached the data needed to
process the task (due to the nodes having recently downloaded the
data from the cloud computing storage platform 104 for a previous
job) and, therefore, be a good candidate for processing the task.
Metadata stored in the database 116 assists the compute service
manager 112 in determining which nodes in the execution platform
114 have already cached at least a portion of the data needed to
process the task. One or more nodes in the execution platform 114
process the task using data cached by the nodes and, if necessary,
data retrieved from the cloud computing storage platform 104. It is
desirable to retrieve as much data as possible from caches within
the execution platform 114 because the retrieval speed is typically
much faster than retrieving data from the cloud computing storage
platform 104.
As shown in FIG. 1, the shared data processing platform 100
separates the execution platform 114 from the cloud computing
storage platform 104. In this arrangement, the processing resources
and cache resources in the execution platform 114 operate
independently of the data storage devices 124-1 to 124-N in the
cloud computing storage platform 104. Thus, the computing resources
and cache resources are not restricted to specific data storage
devices 124-1 to 124-N. Instead, all computing resources and all
cache resources may retrieve data from, and store data to, any of
the data storage resources in the cloud computing storage platform
104.
FIG. 2 is a block diagram illustrating components of the compute
service manager 112, in accordance with some embodiments of the
present disclosure. As shown in FIG. 2, a request processing
service 202 manages received data storage requests and data
retrieval requests (e.g., jobs to be performed on database data).
For example, the request processing service 202 may determine the
data necessary to process a received query (e.g., a data storage
request or data retrieval request). The data may be stored in a
cache within the execution platform 114 or in a data storage device
in cloud computing storage platform 104. A management console
service 204 supports access to various systems and processes by
administrators and other system managers. Additionally, the
management console service 204 may receive a request to execute a
job and monitor the workload on the system. The stream share engine
225 manages change tracking on database objects, such as a data
share (e.g., shared table) or shared view, according to some
example embodiments, and as discussed in further detail below.
The compute service manager 112 also includes a job compiler 206, a
job optimizer 208, and a job executor 210. The job compiler 206
parses a job into multiple discrete tasks and generates the
execution code for each of the multiple discrete tasks. The job
optimizer 208 determines the best method to execute the multiple
discrete tasks based on the data that needs to be processed. The
job optimizer 208 also handles various data pruning operations and
other data optimization techniques to improve the speed and
efficiency of executing the job. The job executor 210 executes the
execution code for jobs received from a queue or determined by the
compute service manager 112.
A job scheduler and coordinator 212 sends received jobs to the
appropriate services or systems for compilation, optimization, and
dispatch to the execution platform 114. For example, jobs may be
prioritized and processed in that prioritized order. In an
embodiment, the job scheduler and coordinator 212 determines a
priority for internal jobs that are scheduled by the compute
service manager 112 with other "outside" jobs such as user queries
that may be scheduled by other systems in the database but may
utilize the same processing resources in the execution platform
114. In some embodiments, the job scheduler and coordinator 212
identifies or assigns particular nodes in the execution platform
114 to process particular tasks. A virtual warehouse manager 214
manages the operation of multiple virtual warehouses implemented in
the execution platform 114. As discussed below, each virtual
warehouse includes multiple execution nodes that each include a
cache and a processor (e.g., a virtual machine, an operating system
level container execution environment).
Additionally, the compute service manager 112 includes a
configuration and metadata manager 216, which manages the
information related to the data stored in the remote data storage
devices and in the local caches (i.e., the caches in execution
platform 114). The configuration and metadata manager 216 uses the
metadata to determine which data micro-partitions need to be
accessed to retrieve data for processing a particular task or job.
A monitor and workload analyzer 218 oversees processes performed by
the compute service manager 112 and manages the distribution of
tasks (e.g., workload) across the virtual warehouses and execution
nodes in the execution platform 114. The monitor and workload
analyzer 218 also redistributes tasks, as needed, based on changing
workloads throughout the network-based data warehouse system 102
and may further redistribute tasks based on a user (e.g.,
"external") query workload that may also be processed by the
execution platform 114. The configuration and metadata manager 216
and the monitor and workload analyzer 218 are coupled to a data
storage device 220. Data storage device 220 in FIG. 2 represent any
data storage device within the network-based data warehouse system
102. For example, data storage device 220 may represent caches in
execution platform 114, storage devices in cloud computing storage
platform 104, or any other storage device.
FIG. 3 is a block diagram illustrating components of the execution
platform 114, in accordance with some embodiments of the present
disclosure. As shown in FIG. 3, execution platform 114 includes
multiple virtual warehouses, which are elastic clusters of compute
instances, such as virtual machines. In the example illustrated,
the virtual warehouses include virtual warehouse 1, virtual
warehouse 2, and virtual warehouse n. Each virtual warehouse (e.g.,
EC2 cluster) includes multiple execution nodes (e.g., virtual
machines) that each include a data cache and a processor. The
virtual warehouses can execute multiple tasks in parallel by using
the multiple execution nodes. As discussed herein, execution
platform 114 can add new virtual warehouses and drop existing
virtual warehouses in real time based on the current processing
needs of the systems and users. This flexibility allows the
execution platform 114 to quickly deploy large amounts of computing
resources when needed without being forced to continue paying for
those computing resources when they are no longer needed. All
virtual warehouses can access data from any data storage device
(e.g., any storage device in cloud computing storage platform
104).
Although each virtual warehouse shown in FIG. 3 includes three
execution nodes, a particular virtual warehouse may include any
number of execution nodes. Further, the number of execution nodes
in a virtual warehouse is dynamic, such that new execution nodes
are created when additional demand is present, and existing
execution nodes are deleted when they are no longer necessary
(e.g., upon a query or job completion).
Each virtual warehouse is capable of accessing any of the data
storage devices 124-1 to 124-N shown in FIG. 1. Thus, the virtual
warehouses are not necessarily assigned to a specific data storage
device 124-1 to 124-N and, instead, can access data from any of the
data storage devices 124-1 to 124-N within the cloud computing
storage platform 104. Similarly, each of the execution nodes shown
in FIG. 3 can access data from any of the data storage devices
124-1 to 124-N. For instance, the storage device 124-1 of a first
user (e.g., provider account user) may be shared with a worker node
in a virtual warehouse of another user (e.g., consumer account
user), such that the other user can create a database (e.g.,
read-only database) and use the data in storage device 124-1
directly without needing to copy the data (e.g., copy it to a new
disk managed by the consumer account user). In some embodiments, a
particular virtual warehouse or a particular execution node may be
temporarily assigned to a specific data storage device, but the
virtual warehouse or execution node may later access data from any
other data storage device.
In the example of FIG. 3, virtual warehouse 1 includes three
execution nodes 302-1, 302-2, and 302-N. Execution node 302-1
includes a cache 304-1 and a processor 306-1. Execution node 302-2
includes a cache 304-2 and a processor 306-2. Execution node 302-N
includes a cache 304-N and a processor 306-N. Each execution node
302-1, 302-2, and 302-N is associated with processing one or more
data storage and/or data retrieval tasks. For example, a virtual
warehouse may handle data storage and data retrieval tasks
associated with an internal service, such as a clustering service,
a materialized view refresh service, a file compaction service, a
storage procedure service, or a file upgrade service. In other
implementations, a particular virtual warehouse may handle data
storage and data retrieval tasks associated with a particular data
storage system or a particular category of data.
Similar to virtual warehouse 1 discussed above, virtual warehouse 2
includes three execution nodes 312-1, 312-2, and 312-N. Execution
node 312-1 includes a cache 314-1 and a processor 316-1. Execution
node 312-2 includes a cache 314-2 and a processor 316-2. Execution
node 312-N includes a cache 314-N and a processor 316-N.
Additionally, virtual warehouse 3 includes three execution nodes
322-1, 322-2, and 322-N. Execution node 322-1 includes a cache
324-1 and a processor 326-1. Execution node 322-2 includes a cache
324-2 and a processor 326-2. Execution node 322-N includes a cache
324-N and a processor 326-N.
In some embodiments, the execution nodes shown in FIG. 3 are
stateless with respect to the data the execution nodes are caching.
For example, these execution nodes do not store or otherwise
maintain state information about the execution node, or the data
being cached by a particular execution node. Thus, in the event of
an execution node failure, the failed node can be transparently
replaced by another node. Since there is no state information
associated with the failed execution node, the new (replacement)
execution node can easily replace the failed node without concern
for recreating a particular state.
Although the execution nodes shown in FIG. 3 each include one data
cache and one processor, alternative embodiments may include
execution nodes containing any number of processors and any number
of caches. Additionally, the caches may vary in size among the
different execution nodes. The caches shown in FIG. 3 store, in the
local execution node (e.g., local disk), data that was retrieved
from one or more data storage devices in cloud computing storage
platform 104 (e.g., S3 objects recently accessed by the given
node). In some example embodiments, the cache stores file headers
and individual columns of files as a query downloads only columns
necessary for that query.
To improve cache hits and avoid overlapping redundant data stored
in the node caches, the job optimizer 208 assigns input file sets
to the nodes using a consistent hashing scheme to hash over table
file names of the data accessed (e.g., data in database 116 or
database 122). Subsequent or concurrent queries accessing the same
table file will therefore be performed on the same node, according
to some example embodiments.
As discussed, the nodes and virtual warehouses may change
dynamically in response to environmental conditions (e.g., disaster
scenarios), hardware/software issues (e.g., malfunctions), or
administrative changes (e.g., changing from a large cluster to
smaller cluster to lower costs). In some example embodiments, when
the set of nodes changes, no data is reshuffled immediately.
Instead, the least recently used replacement policy is implemented
to eventually replace the lost cache contents over multiple jobs.
Thus, the caches reduce or eliminate the bottleneck problems
occurring in platforms that consistently retrieve data from remote
storage systems. Instead of repeatedly accessing data from the
remote storage devices, the systems and methods described herein
access data from the caches in the execution nodes, which is
significantly faster and avoids the bottleneck problem discussed
above. In some embodiments, the caches are implemented using
high-speed memory devices that provide fast access to the cached
data. Each cache can store data from any of the storage devices in
the cloud computing storage platform 104.
Further, the cache resources and computing resources may vary
between different execution nodes. For example, one execution node
may contain significant computing resources and minimal cache
resources, making the execution node useful for tasks that require
significant computing resources. Another execution node may contain
significant cache resources and minimal computing resources, making
this execution node useful for tasks that require caching of large
amounts of data. Yet another execution node may contain cache
resources providing faster input-output operations, useful for
tasks that require fast scanning of large amounts of data. In some
embodiments, the execution platform 114 implements skew handling to
distribute work amongst the cache resources and computing resources
associated with a particular execution, where the distribution may
be further based on the expected tasks to be performed by the
execution nodes. For example, an execution node may be assigned
more processing resources if the tasks performed by the execution
node become more processor-intensive. Similarly, an execution node
may be assigned more cache resources if the tasks performed by the
execution node require a larger cache capacity. Further, some nodes
may be executing much slower than others due to various issues
(e.g., virtualization issues, network overhead). In some example
embodiments, the imbalances are addressed at the scan level using a
file stealing scheme. In particular, whenever a node process
completes scanning its set of input files, it requests additional
files from other nodes. If the one of the other nodes receives such
a request, the node analyzes its own set (e.g., how many files are
left in the input file set when the request is received), and then
transfers ownership of one or more of the remaining files for the
duration of the current job (e.g., query). The requesting node
(e.g., the file stealing node) then receives the data (e.g., header
data) and downloads the files from the cloud computing storage
platform 104 (e.g., from data storage device 124-1), and does not
download the files from the transferring node. In this way, lagging
nodes can transfer files via file stealing in a way that does not
worsen the load on the lagging nodes.
Although virtual warehouses 1, 2, and n are associated with the
same execution platform 114, the virtual warehouses may be
implemented using multiple computing systems at multiple geographic
locations. For example, virtual warehouse 1 can be implemented by a
computing system at a first geographic location, while virtual
warehouses 2 and n are implemented by another computing system at a
second geographic location. In some embodiments, these different
computing systems are cloud-based computing systems maintained by
one or more different entities.
Additionally, each virtual warehouse is shown in FIG. 3 as having
multiple execution nodes. The multiple execution nodes associated
with each virtual warehouse may be implemented using multiple
computing systems at multiple geographic locations. For example, an
instance of virtual warehouse 1 implements execution nodes 302-1
and 302-2 on one computing platform at a geographic location and
implements execution node 302-N at a different computing platform
at another geographic location. Selecting particular computing
systems to implement an execution node may depend on various
factors, such as the level of resources needed for a particular
execution node (e.g., processing resource requirements and cache
requirements), the resources available at particular computing
systems, communication capabilities of networks within a geographic
location or between geographic locations, and which computing
systems are already implementing other execution nodes in the
virtual warehouse.
Execution platform 114 is also fault tolerant. For example, if one
virtual warehouse fails, that virtual warehouse is quickly replaced
with a different virtual warehouse at a different geographic
location.
A particular execution platform 114 may include any number of
virtual warehouses. Additionally, the number of virtual warehouses
in a particular execution platform is dynamic, such that new
virtual warehouses are created when additional processing and/or
caching resources are needed. Similarly, existing virtual
warehouses may be deleted when the resources associated with the
virtual warehouse are no longer necessary.
In some embodiments, the virtual warehouses may operate on the same
data in cloud computing storage platform 104, but each virtual
warehouse has its own execution nodes with independent processing
and caching resources. This configuration allows requests on
different virtual warehouses to be processed independently and with
no interference between the requests. This independent processing,
combined with the ability to dynamically add and remove virtual
warehouses, supports the addition of new processing capacity for
new users without impacting the performance observed by the
existing users.
Next, techniques for real-time streaming data ingestion into a data
system will be described. FIG. 4 shows an example of a computing
environment, according to some example embodiments. The computing
environment may include a client 402, a storage 404, a query global
service (GS) 406, a commit service 408, and a database 410.
The client 402 may include a software development kit (SDK) to run
software programs to generate the file structures described herein.
The client 402 may communicate with the data system to upload data
to be ingested into one or more tables. The client 402 may open one
or more channels to the data system. A channel may be a logical
connection to a particular table stored in the data system, such as
in the database 410. The data system, e.g., in the database 410,
may include a plurality of tables associated with an account for
the client 402.
The client 402 may write new data, such as rows, into the one or
more channels. The client 402 may include a buffer. The client 402
may buffer the outgoing data into per-table sets (also referred to
as "chunks" herein). A chunk may be associated with a single table
(i.e., all data in a chunk is addressed to a single table);
however, a table may be associated with a plurality of chunks at a
time. The data in the chunks may be provided in a first format. For
example, the data in the chunks may be provided in the format used
by the client 402 (e.g., Arrow format). The chunks may be buffered
into per-account groups (also referred to as "blobs" herein). A
blob may contain chunks associated with different tables of the
same account.
The buffer may include a threshold, e.g., a size or time threshold.
When the threshold is crossed or exceeded, the client 402 may
transfer the blobs in its buffer to the storage 404. The storage
404 may be provided as cloud storage and may be a part of the data
system. Additionally or alternatively, the storage 404 may be
provided as internal storage of the data system. In another
embodiment, the storage 404 may be provided as external storage to
the data system. In any event, the data system may have access to
the storage 404. The client 402 may write the blobs to an internal
stage in the storage 404. The client 402 may use an API for the
storage 404 to perform the write operation. The storage 404 may
receive the blobs from the client 402 and may store them in the
first format.
The client 402 may also transmit a registration request to the
query GS 406 in the data system. The registration request may
include information about the blobs stored at the storage 404, such
as identification information for the blobs and the location where
the blobs are stored (e.g., network address). The client 402 may
register the blobs via a REST API call to the query GS 406.
The query GS 406 may communicate with the commit service 408. The
query GS 406 may fan the blob registration requests into per-table
chunk registration requests and transmit the per-table chunk
registration requests to the commit service 408. The commit service
408 may queue the per-table chunks and may validate and dedupe them
using sequencing information, described in further detail below.
The commit service 408 may fast commit the data via RPC to generate
a hybrid table. The commit service 408 may write the data to a
metadata store to commit the data. The committing of the data may
be used to generate a hybrid table, which can be used for immediate
query processing of the committed data. The hybrid table may
include data from the blobs in a first format (e.g., Arrow format)
and data in the one or more tables stored in database 410 in a
second format (e.g., FDN). The hybrid table may make the data in
the blobs in the storage 404 available for query processing, as
described in further detail below.
The database 410 may store the tables associated with different
accounts. Each account may have one or more tables associated
therewith. The data stored in the blobs (organized by per-table
chunks) in the storage 404 may be ingested into corresponding
tables in the database 410 after it has been committed. After the
data is ingested into the corresponding tables in the database 410,
that data may be removed from the storage 404 (e.g., flushed).
FIG. 5 shows a flow diagram of a method 500 for generating a blob,
according to some example embodiments. In an example, portions of
the method 500 may be performed by the client 402 (e.g., using a
client SDK). At operation 502, the client may open one or more
channels to a table stored in the data system. The number of
channels may depend on the amount of data to be transferred to the
data system. The number of channels may depend on the number of
tables implicated in the data transfer. At operation 504, the
client may write data (e.g., rows) into the channels. The data may
be in a first format (e.g., Arrow format). At operation 506, the
data may be buffered into per-table chunks in the first format.
Hence, each chunk may contain data for only one table. At operation
508, the chunks may be buffered into per-account blobs. Hence, each
blob may contain data for one or more tables but for only one
account. An account may have a plurality of tables associated with
it in the data system.
The client may also insert ordering or sequencing information. For
example, the client may insert client and row sequencing
information. The client sequencing information may be related to
the channel. Each channel may be client specific; hence, only one
client may have ownership of a channel at a time. The client
sequencing information may prevent concurrent usage of the channel
by multiple clients (accounts). The data system may use the client
sequencing information to identify the present owner of the channel
and prevent other accounts from using the same channel.
The row sequencing information may provide information related to
each record or row. Each record or row may be stamped with a row
sequencer. This row sequencing information may then be used by the
data system to check for duplicate data or gaps in the data. For
example, if the data system receives two records with the same row
sequencing information, it may detect a duplicate. On the other
hand, if the data system receives record 1 and then record 3, it
may detect that it did not receive record 2. Also, the row
sequencing information may be used to maintain the ordering of the
data.
The client may also insert an offset token. The client may use the
offset token information in case there was an error in the blob
transmission. In the case of a client failure, the offset token may
indicate which data was correctly received by the data system, so
the client may restart its transmission without having to duplicate
already received data and without including gaps in the data.
The buffer may have a threshold associated therewith. For example,
the buffer may include a size and/or time threshold. At operation
510, the client may write the blob into storage. For example, the
client may write the blob into storage in response to the buffer's
threshold being exceeded. The client may write the blob to an
internal stage associated with the account in the storage (e.g.,
Streaming Ingest internal stage). The client may use storage API to
perform the write operation.
At operation 512, the client may register the blob with the query
GS in the data system. The client may transmit a registration
request to the query GS. The registration request may include
information about the blob, such as identification information for
the blob and the location where the blob is stored, such as an
address. The client may register the blob via a REST API call to
the query GS. After the data system registers the blob and commits
the data therein, it may transmit a confirmation to the client. The
client may receive the confirmation. Once the data is committed and
before it is ingested into the source table, that data may be
available for query processing. From the client perspective, the
committed data and data in the tables may be available the same way
for query processing.
FIG. 6A shows an example of a blob 600, according to some example
embodiments. The blob 600 may include a header 602 and one or more
chunks 604, 606, 608, 610. The header 602 may include information
relating blob version, client sequencing (e.g., client sequence
number), row sequencing (e.g., row sequence number), tables
contained in blob, offset token, and byte ranges of the chunks. The
header may also include expression property (EP) information about
the chunks. The EP information may include statistics for the
chunks 604-610. For example, the EP information may include min-max
of a chunk, number of rows, and other statistics. As discussed in
further detail below, the data system may use this EP information
for optimizing and pruning query processing for the committed
data.
As mentioned above, the chunks may be defined per-table. For
example, chunk 604 may be associated with table A; chunks 606 and
608 may be associated with table B; and chunk 610 may be associated
with table C. The data in the blob 600 may be in the native (first)
format of the client (e.g., Arrow format).
FIG. 6B shows another example of a blob 650, according to some
example embodiments. The blob 650 may include a blob name field
652, a version field 654, a file size field 656, a checksum field
658, chunks metadata length field 660, and chunks metadata field
662. The blob 650 may also then include chunk data and EP
information about each chunk. For example, blob 650 may include
Chunk 1 EP Data 664 and Chunk 1 Data 666, Chunk 2 EP Data 668 and
Chunk 2 Data 670, and Chunk 3 EP Data 672 and Chunk 3 EP Data 674.
The data in the chunks may be in the native (first) format of the
client (e.g., Arrow format).
FIG. 7 shows a flow diagram of a method 700 for committing a blob,
according to some example embodiments. At operation 702, the data
system (e.g., query GS) may receive a registration request for the
blob. The registration request may include information about the
blob, such as identification information for the blob and the
location where the blob is stored, such as an address.
At operation 704, the data system may access the stored blob using
the information provided in the registration request. The data in
the blob (e.g., chunks) may be stored in their native format, which
is referred to as a first format herein. At operation 706, the
query GS may divide the blob registration request into per-table
chunk registration requests and transmit the per-table chunk
registration requests to the commit service. At operation 708, the
commit service may queue the per-table chunks and may validate and
dedupe the data using sequencing information. For example, the
commit service may use the client and row sequencing information to
validate the incoming data. If an error is detected, the data
system may send a notification to the client. For example, the data
system may transmit the most recent offset token information to the
client, instructing the client to transmit the data based on the
offset token information.
At operation 710, the commit service may commit the data via RPC to
generate a hybrid table. Data may be written to a metadata store to
commit the incoming data. The hybrid table may include data from
the chunks in a first format (e.g., Arrow format) and data in the
one or more tables stored in database associated with the common
account in a second format (e.g., FDN).
At operation 712, the commit service may make the data in the blob
available for query processing, as described in further detail
below. The hybrid table may allow query processing of data in the
blob which is not yet ingested into source tables.
At operation 714, the committed data may be ingested or migrated
into their corresponding source table. The ingested data may create
new partitions in the source table. The ingestion may be performed
using a variety of techniques. In one embodiment, DML operation on
the committed data may initiate ingestion. For example, if a DML
operation touches upon a section (e.g., a row) of the committed
data, that section or the chunk associated with that section may be
ingested or migrated to the source table in response to the DML
operation. Moreover, a background service may also operate to
ingest the committed data at specified times (e.g., intervals).
After the committed data has been ingested, that data may be
removed from storage.
In the embodiments described above, blob generation was handled
primarily by the client (e.g., using client SDK). However, some or
all blob generation responsibilities may be performed by the data
system. FIG. 8 shows an example of a computing environment,
according to some example embodiments. The computing environment
may include a client 802, a storage 804, a query global service
(GS) 806, a commit service 808, a database 810, and a buffer
service 812.
Here, the client 802 may be less robust as the client 402 described
above with reference to FIG. 4. The client 802 may communicate with
the data system via HTTP calls using REST API, a thin client SDK
wrapper, or the like. For example, the client 802 may be provided
as Intent of Things (JOT) device, such as an appliance, lights,
etc., which may have limited processing and communication
capabilities. The client 802 may communicate with the data system
to upload data to be ingested into one or more tables. The client
802 may open one or more channels to the data system. A channel may
be a logical connection to a particular table stored in the data
system. The data system may include a plurality of tables
associated with an account for the client 802.
The client 802 may write new data, such as rows, into the one or
more channels. The client 802 may transmit the data to the query GS
806. The client 802 may also insert sequencing information (e.g.,
channel and row sequence numbers) and/or offset token information,
as described above.
The query service 806 may forward the data received from the client
802 to the buffer service 812. The buffer service 812 may generate
chunks and blobs from the received data as described herein. The
buffer service 812 may validate, dedupe, and aggregate data (e.g.,
rows) from one or more channels associated with the account for the
client 802 into per-table sets (also referred to as "chunks"
herein). The data in the chunks may be provided in a first format.
For example, the data in the chunks may be provided in the format
used by the client 802 (e.g., Arrow format). The chunks may be
arranged into per-account groups (also referred to as "blobs"
herein).
The buffer service 812 may include a threshold, e.g., a size or
time threshold. When the threshold is crossed or exceeded, the
buffer service 812 may write the blobs to the storage 804. The
storage 804 may be provided as cloud storage and may be a part of
the data system. Additionally or alternatively, the storage 804 may
be provided as internal storage of the data system. In another
embodiment, the storage 804 may be provided as external storage to
the data system. In any event, the data system may have access to
the storage 804. The storage 804 may receive the blobs from the
client 802 and may store them in the first format. The buffer
service 812 may write the blobs to an internal stage in the storage
804. The buffer service 812 may use an API for the storage 804 to
perform the write operation.
The buffer service 812 may communicate with the commit service 808.
The buffer service 812 may fan out the per-table chunk registration
requests and transmit the per-table chunk registration requests to
the commit service 808. The buffer service 812 may provide
information for the blobs and the location where the blobs are
stored, such as an address. The commit service 808 may queue the
per-table chunks and may validate and dedupe then using sequencing
information. The commit service 808 may fast commit the data via
RPC to generate a hybrid table. The query service 808 may write the
data to a metadata store to commit the data. The hybrid table may
include data from the blobs in a first format (e.g., Arrow format)
and data in the one or more tables stored in database 810 in a
second format (e.g., FDN). The hybrid table may make the data in
the blobs in the storage 804 available for query processing before
the data is ingested into the one or more tables stored in the
database 810, as described in further detail below.
The database 810 may store the tables associated with different
accounts. Each account may have one or more tables associated
therewith. The data stored in the blobs (organized by per-table
chunks) in the storage 804 may be ingested into corresponding
tables in the database 810. After the data is ingested into the
corresponding tables in the database 810, that data may be removed
from the storage 804.
FIG. 9 shows a flow diagram of a method 900 for generating and
committing a blob, according to some example embodiments. The
method 900 may be executed by a data system and its components. At
902, the data system may receive the incoming data from a client
via one or more channels, for example, as described above with
reference to FIG. 8. The incoming data may be received via a HTTP
call. The incoming data may include client and row sequencing
information and offset token information. The incoming data may
also include related EP information.
At operation 904, the data may be buffered into per-table chunks in
the first format. Hence, each chunk may contain data for only one
table. At operation 906, the chunks may be buffered into
per-account blobs. Hence, each blob may contain data for one or
more tables but for only one account. An account may have a
plurality of tables associated with it in the data system. The data
system may include a buffer having a threshold associated
therewith. For example, the buffer may include a size and/or time
threshold.
At operation 908, the data system (e.g., buffer service) may write
the blob into storage. For example, the buffer service may write
the blob into storage in response to the buffer's threshold being
exceeded. The buffer service may write the blob to an internal
stage associated with the account in the storage (e.g., Streaming
Ingest internal stage). The buffer service may use storage API to
perform the write operation. The data in the blob (e.g., chunks)
may be stored in their native format, which is referred to as a
first format herein.
At 910, the data system (e.g., buffer service or query GS) may
generate per-table chunk registration requests and transmit the
per-table chunk registration requests to the commit service. At
912, the commit service may queue the per-table chunks and may
validate and dedupe the data using sequencing information. For
example, the commit service may use the client and row sequencing
information to validate the incoming data. If an error is detected,
the data system may send a notification to the client. For example,
the data system may transmit the most recent offset token
information to the client, instructing the client to transmit the
data based on the offset token information.
At operation 914, the commit service may commit the data via RPC to
generate a hybrid table. Data may be written to a metadata store to
commit the incoming data. The hybrid table may include data from
the chunks in a first format (e.g., Arrow format) and data in the
one or more tables stored in a database in a second format (e.g.,
FDN).
At operation 916, the commit service may make the data in the blob
available for query processing, as described in further detail
below. The hybrid table may allow query processing of data in the
blob which is not yet ingested into source tables.
At operation 918, the committed data may be ingested or migrated
into their corresponding source table. The ingested data may create
new partitions in the source table. The ingestion may be performed
using a variety of techniques. In one embodiment, DML operation on
the committed data may initiate ingestion. For example, if a DML
operation touches upon a section (e.g., a row) of the committed
data, that section or the chunk associated with that section may be
ingested or migrated to the source table in response to the DML
operation. Moreover, a background service may also operate to
ingest the committed data at specified times (e.g., intervals).
After committed data has been ingested, that data may be removed
from storage.
FIG. 10 shows a flow diagram of a method 1000 for processing a
query, according to some example embodiments. At operation 1002,
the data system may receive a query. The query may relate to data
stored in a source table stored in the data system and data
committed for that table but not yet ingested into the source
table, as described herein. Hence, the data system may process the
query using both the data stored in the source table and the
committed data stored in storage as a hybrid table, as described
herein. The data may be provided into two formats, as described
herein. The committed data may be stored in a first format (e.g.,
Arrow) and the data in the source table may be stored in a second
format (e.g., FDN).
At operation 1004, the data for the query may be partitioned into
different scansets, e.g., one for the data in the source table and
one for the committed data. At operation 1006, the scansets may be
pruned based on expression properties of the scansets. For example,
the EP information associated with the chunks in the blobs of the
committed data may be used to prune the committed data scanset. At
operation 1008, the different scansets may be scanned and may be
converted to a common format. The different format information may
be converted to a common in-memory format during the scanning. At
operation 1010, the different scansets, now converted to the common
in-memory format, may be joined (e.g., union operator) for query
execution. This joining of the committed data and source table data
may present a unified view of the data by way of the hybrid table.
At operation 1012, the query may be executed using the joined data,
and a result of the query may be generated and transmitted to the
requester of the query.
The techniques described herein provide benefits over other
ingestion techniques. The techniques described herein provide
direct data streaming to source tables over http calls without
using other complex components. Hence, the techniques provide lower
overheard with minimal configuration while providing high
throughput. The techniques maintain ordering information of the new
data. The ordering information from the client is maintained, and
no other ordering may not be performed by the data system.
Moreover, the techniques provide low latency and low cost. The new
data is available for query after it is committed and before it is
ingested as described herein (e.g., the use of the hybrid table).
Thus, the data may be available for querying almost immediately
(e.g., a few seconds).
FIG. 11 illustrates a diagrammatic representation of a machine 1100
in the form of a computer system within which a set of instructions
may be executed for causing the machine 1100 to perform any one or
more of the methodologies discussed herein, according to an example
embodiment. Specifically, FIG. 11 shows a diagrammatic
representation of the machine 1100 in the example form of a
computer system, within which instructions 1116 (e.g., software, a
program, an application, an applet, an app, or other executable
code) for causing the machine 1100 to perform any one or more of
the methodologies discussed herein may be executed. For example,
the instructions 1116 may cause the machine 1100 to execute any one
or more operations of any one or more of the methods described
herein. As another example, the instructions 1116 may cause the
machine 1100 to implement portions of the data flows described
herein. In this way, the instructions 1116 transform a general,
non-programmed machine into a particular machine 1100 (e.g., the
remote computing device 106, the access management system 110, the
compute service manager 112, the execution platform 114, the access
management system 118, the Web proxy 120, remote computing device
106) that is specially configured to carry out any one of the
described and illustrated functions in the manner described
herein.
In alternative embodiments, the machine 1100 operates as a
standalone device or may be coupled (e.g., networked) to other
machines. In a networked deployment, the machine 1100 may operate
in the capacity of a server machine or a client machine in a
server-client network environment, or as a peer machine in a
peer-to-peer (or distributed) network environment. The machine 1100
may comprise, but not be limited to, a server computer, a client
computer, a personal computer (PC), a tablet computer, a laptop
computer, a netbook, a smart phone, a mobile device, a network
router, a network switch, a network bridge, or any machine capable
of executing the instructions 1116, sequentially or otherwise, that
specify actions to be taken by the machine 1100. Further, while
only a single machine 1100 is illustrated, the term "machine" shall
also be taken to include a collection of machines 1100 that
individually or jointly execute the instructions 1116 to perform
any one or more of the methodologies discussed herein.
The machine 1100 includes processors 1110, memory 1130, and
input/output (I/O) components 1150 configured to communicate with
each other such as via a bus 1102. In an example embodiment, the
processors 1110 (e.g., a central processing unit (CPU), a reduced
instruction set computing (RISC) processor, a complex instruction
set computing (CISC) processor, a graphics processing unit (GPU), a
digital signal processor (DSP), an application-specific integrated
circuit (ASIC), a radio-frequency integrated circuit (RFIC),
another processor, or any suitable combination thereof) may
include, for example, a processor 1112 and a processor 1114 that
may execute the instructions 1116. The term "processor" is intended
to include multi-core processors 1110 that may comprise two or more
independent processors (sometimes referred to as "cores") that may
execute instructions 1116 contemporaneously. Although FIG. 11 shows
multiple processors 1110, the machine 1100 may include a single
processor with a single core, a single processor with multiple
cores (e.g., a multi-core processor), multiple processors with a
single core, multiple processors with multiple cores, or any
combination thereof.
The memory 1130 may include a main memory 1132, a static memory
1134, and a storage unit 1136, all accessible to the processors
1110 such as via the bus 1102. The main memory 1132, the static
memory 1134, and the storage unit 1136 store the instructions 1116
embodying any one or more of the methodologies or functions
described herein. The instructions 1116 may also reside, completely
or partially, within the main memory 1132, within the static memory
1134, within the storage unit 1136, within at least one of the
processors 1110 (e.g., within the processor's cache memory), or any
suitable combination thereof, during execution thereof by the
machine 1100.
The I/O components 1150 include components to receive input,
provide output, produce output, transmit information, exchange
information, capture measurements, and so on. The specific I/O
components 1150 that are included in a particular machine 1100 will
depend on the type of machine. For example, portable machines such
as mobile phones will likely include a touch input device or other
such input mechanisms, while a headless server machine will likely
not include such a touch input device. It will be appreciated that
the I/O components 1150 may include many other components that are
not shown in FIG. 11. The I/O components 1150 are grouped according
to functionality merely for simplifying the following discussion
and the grouping is in no way limiting. In various example
embodiments, the I/O components 1150 may include output components
1152 and input components 1154. The output components 1152 may
include visual components (e.g., a display such as a plasma display
panel (PDP), a light emitting diode (LED) display, a liquid crystal
display (LCD), a projector, or a cathode ray tube (CRT)), acoustic
components (e.g., speakers), other signal generators, and so forth.
The input components 1154 may include alphanumeric input components
(e.g., a keyboard, a touch screen configured to receive
alphanumeric input, a photo-optical keyboard, or other alphanumeric
input components), point-based input components (e.g., a mouse, a
touchpad, a trackball, a joystick, a motion sensor, or another
pointing instrument), tactile input components (e.g., a physical
button, a touch screen that provides location and/or force of
touches or touch gestures, or other tactile input components),
audio input components (e.g., a microphone), and the like.
Communication may be implemented using a wide variety of
technologies. The I/O components 1150 may include communication
components 1164 operable to couple the machine 1100 to a network
1180 or devices 1170 via a coupling 1182 and a coupling 1172,
respectively. For example, the communication components 1164 may
include a network interface component or another suitable device to
interface with the network 1180. In further examples, the
communication components 1164 may include wired communication
components, wireless communication components, cellular
communication components, and other communication components to
provide communication via other modalities. The devices 1170 may be
another machine or any of a wide variety of peripheral devices
(e.g., a peripheral device coupled via a universal serial bus
(USB)). For example, as noted above, the machine 1100 may
correspond to any one of the remote computing device 106, the
access management system 110, the compute service manager 112, the
execution platform 114, the access management system 118, the Web
proxy 120, and the devices 1170 may include any other of these
systems and devices.
The various memories (e.g., 1130, 1132, 1134, and/or memory of the
processor(s) 1110 and/or the storage unit 1136) may store one or
more sets of instructions 1116 and data structures (e.g., software)
embodying or utilized by any one or more of the methodologies or
functions described herein. These instructions 1116, when executed
by the processor(s) 1110, cause various operations to implement the
disclosed embodiments.
As used herein, the terms "machine-storage medium," "device-storage
medium," and "computer-storage medium" mean the same thing and may
be used interchangeably in this disclosure. The terms refer to a
single or multiple storage devices and/or media (e.g., a
centralized or distributed database, and/or associated caches and
servers) that store executable instructions and/or data. The terms
shall accordingly be taken to include, but not be limited to,
solid-state memories, and optical and magnetic media, including
memory internal or external to processors. Specific examples of
machine-storage media, computer-storage media, and/or
device-storage media include non-volatile memory, including by way
of example semiconductor memory devices, e.g., erasable
programmable read-only memory (EPROM), electrically erasable
programmable read-only memory (EEPROM), field-programmable gate
arrays (FPGAs), and flash memory devices; magnetic disks such as
internal hard disks and removable disks; magneto-optical disks; and
CD-ROM and DVD-ROM disks. The terms "machine-storage media,"
"computer-storage media," and "device-storage media" specifically
exclude carrier waves, modulated data signals, and other such
media, at least some of which are covered under the term "signal
medium" discussed below.
In various example embodiments, one or more portions of the network
1180 may be an ad hoc network, an intranet, an extranet, a virtual
private network (VPN), a local-area network (LAN), a wireless LAN
(WLAN), a wide-area network (WAN), a wireless WAN (WWAN), a
metropolitan-area network (MAN), the Internet, a portion of the
Internet, a portion of the public switched telephone network
(PSTN), a plain old telephone service (POTS) network, a cellular
telephone network, a wireless network, a Wi-Fi.RTM. network,
another type of network, or a combination of two or more such
networks. For example, the network 1180 or a portion of the network
1180 may include a wireless or cellular network, and the coupling
1182 may be a Code Division Multiple Access (CDMA) connection, a
Global System for Mobile communications (GSM) connection, or
another type of cellular or wireless coupling. In this example, the
coupling 1182 may implement any of a variety of types of data
transfer technology, such as Single Carrier Radio Transmission
Technology (1.times.RTT), Evolution-Data Optimized (EVDO)
technology, General Packet Radio Service (GPRS) technology,
Enhanced Data rates for GSM Evolution (EDGE) technology, third
Generation Partnership Project (3GPP) including 3G, fourth
generation wireless (4G) networks, Universal Mobile
Telecommunications System (UMTS), High-Speed Packet Access (HSPA),
Worldwide Interoperability for Microwave Access (WiMAX), Long Term
Evolution (LTE) standard, others defined by various
standard-setting organizations, other long-range protocols, or
other data transfer technology.
The instructions 1116 may be transmitted or received over the
network 1180 using a transmission medium via a network interface
device (e.g., a network interface component included in the
communication components 1164) and utilizing any one of a number of
well-known transfer protocols (e.g., hypertext transfer protocol
(HTTP)). Similarly, the instructions 1116 may be transmitted or
received using a transmission medium via the coupling 1172 (e.g., a
peer-to-peer coupling) to the devices 1170. The terms "transmission
medium" and "signal medium" mean the same thing and may be used
interchangeably in this disclosure. The terms "transmission medium"
and "signal medium" shall be taken to include any intangible medium
that is capable of storing, encoding, or carrying the instructions
1116 for execution by the machine 1100, and include digital or
analog communications signals or other intangible media to
facilitate communication of such software. Hence, the terms
"transmission medium" and "signal medium" shall be taken to include
any form of modulated data signal, carrier wave, and so forth. The
term "modulated data signal" means a signal that has one or more of
its characteristics set or changed in such a manner as to encode
information in the signal.
The terms "machine-readable medium," "computer-readable medium,"
and "device-readable medium" mean the same thing and may be used
interchangeably in this disclosure. The terms are defined to
include both machine-storage media and transmission media. Thus,
the terms include both storage devices/media and carrier
waves/modulated data signals.
The various operations of example methods described herein may be
performed, at least partially, by one or more processors that are
temporarily configured (e.g., by software) or permanently
configured to perform the relevant operations. Similarly, the
methods described herein may be at least partially
processor-implemented. For example, at least some of the operations
of the methods described herein may be performed by one or more
processors. The performance of certain of the operations may be
distributed among the one or more processors, not only residing
within a single machine, but also deployed across a number of
machines. In some example embodiments, the processor or processors
may be located in a single location (e.g., within a home
environment, an office environment, or a server farm), while in
other embodiments the processors may be distributed across a number
of locations.
Although the embodiments of the present disclosure have been
described with reference to specific example embodiments, it will
be evident that various modifications and changes may be made to
these embodiments without departing from the broader scope of the
inventive subject matter. Accordingly, the specification and
drawings are to be regarded in an illustrative rather than a
restrictive sense. The accompanying drawings that form a part
hereof show, by way of illustration, and not of limitation,
specific embodiments in which the subject matter may be practiced.
The embodiments illustrated are described in sufficient detail to
enable those skilled in the art to practice the teachings disclosed
herein. Other embodiments may be used and derived therefrom, such
that structural and logical substitutions and changes may be made
without departing from the scope of this disclosure. This Detailed
Description, therefore, is not to be taken in a limiting sense, and
the scope of various embodiments is defined only by the appended
claims, along with the full range of equivalents to which such
claims are entitled.
Such embodiments of the inventive subject matter may be referred to
herein, individually and/or collectively, by the term "invention"
merely for convenience and without intending to voluntarily limit
the scope of this application to any single invention or inventive
concept if more than one is in fact disclosed. Thus, although
specific embodiments have been illustrated and described herein, it
should be appreciated that any arrangement calculated to achieve
the same purpose may be substituted for the specific embodiments
shown. This disclosure is intended to cover any and all adaptations
or variations of various embodiments. Combinations of the above
embodiments, and other embodiments not specifically described
herein, will be apparent, to those of skill in the art, upon
reviewing the above description.
In this document, the terms "a" or "an" are used, as is common in
patent documents, to include one or more than one, independent of
any other instances or usages of "at least one" or "one or more."
In this document, the term "or" is used to refer to a nonexclusive
or, such that "A or B" includes "A but not B," "B but not A," and
"A and B," unless otherwise indicated. In the appended claims, the
terms "including" and "in which" are used as the plain-English
equivalents of the respective terms "comprising" and "wherein."
Also, in the following claims, the terms "including" and
"comprising" are open-ended; that is, a system, device, article, or
process that includes elements in addition to those listed after
such a term in a claim is still deemed to fall within the scope of
that claim.
The following numbered examples are embodiments:
Example 1. A method comprising: receiving data from a client via
one or more channels for ingestion into one or more source tables
in a data system; storing the received data in a storage in a first
format; based on a registration request for the received data,
committing, by a processor, the received data stored in the storage
and making the received data in the first format accessible for
query processing before the received data is ingested into the one
or more source tables; and ingesting the received data into the one
or more source tables in a second format.
Example 2. The method of example 2, wherein the received data is
organized into per-table sets, data in each set belonging to a
single source table.
Example 3. The method of any of examples 1-2, wherein per-table
sets are organized into per-account groups, data in each group
belonging to a single account.
Example 4. The method of any of examples 1-3, further comprising:
writing the received data to a metadata store; and generating a
hybrid table for query processing, the hybrid table including the
committed data in the first format and data from the one or more
source tables in the second table.
Example 5. The method of any of examples 1-4, further comprising:
for query processing: converting the committed data from the first
format into a common format; converting the data from the one or
more source tables into the common format; joining the committed
data in the common format and the data from the one or more source
tables in the common format to generate joined data; and executing
a query based on the joined data.
Example 6. The method of any of examples 1-5, further comprising:
retrieving expression properties of the received data; and pruning
the received data based on the expression properties and the
query.
Example 7. The method of any of examples 1-6, wherein the received
data includes sequencing information.
Example 8. The method of any of examples 1-7, wherein ordering of
the received data is maintained based on the sequencing
information.
Example 9. A system comprising: at least one hardware processor;
and at least one memory storing instructions that, when executed by
the at least one hardware processor, cause the at least one
hardware processor to perform operations implementing any one of
example methods 1 to 8.
Example 10. A machine-readable storage device embodying
instructions that, when executed by a machine, cause the machine to
perform operations implementing any one of example methods 1 to
8.
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